2018
DOI: 10.19080/bboaj.2018.04.555649
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Be Wary of Using Poisson Regression to Estimate Risk and Relative Risk

Abstract: Fitting a log binomial model to binary outcome data makes it possible to estimate risk and relative risk for follow-up data, and prevalence and prevalence ratios for cross-sectional data. However, the fitting algorithm may fail to converge when the maximum likelihood solution is on the boundary of the allowable parameter space. Some authorities recommend switching to Poisson regression with robust standard errors to approximate the coefficients of the log binomial model in those circumstances. This solves the … Show more

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Cited by 6 publications
(2 citation statements)
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“…As for binary outcomes, to avoid the issue of poor performance of the model in case of solutions near the boundary described in Zhu et al [45], Poisson regression models with robust standard errors, having the variable "intervention allocation," each variable separately, and its interaction with treatment as regressors, were performed using the Bonferroni correction to take multiple testing into account. Originally, further analyses stratifying by method of assessment (face-to-face vs. telephone or secure online audio/video communication) were planned.…”
Section: Discussionmentioning
confidence: 99%
“…As for binary outcomes, to avoid the issue of poor performance of the model in case of solutions near the boundary described in Zhu et al [45], Poisson regression models with robust standard errors, having the variable "intervention allocation," each variable separately, and its interaction with treatment as regressors, were performed using the Bonferroni correction to take multiple testing into account. Originally, further analyses stratifying by method of assessment (face-to-face vs. telephone or secure online audio/video communication) were planned.…”
Section: Discussionmentioning
confidence: 99%
“…A Poisson regression model with robust variance estimation was used to assess factors associated with the outcome variable. The Poisson regression analysis model is an appropriate analytical model for estimating the prevalence ratio (PR) in crosssectional studies binary and common outcomes (34). The backward regression was fitted with selected explanatory variables.…”
Section: Data Management and Analysismentioning
confidence: 99%